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Bacterial C-type haem-copper oxidases in the cbb3 family are widespread in microaerophiles, which exploit their high oxygen-binding affinity for growth in microoxic niches. In microaerophilic pathogens, C-type oxidases can be essential for infection, yet little is known about their biogenesis compared to model bacteria. Here, we have identified genes involved in cbb3-oxidase (Cco) assembly and activity in the Gram-negative pathogen Campylobacter jejuni, the commonest cause of human food-borne bacterial gastroenteritis. Several genes of unknown function downstream of the oxidase structural genes ccoNOQP were shown to be essential (cj1483c and cj1486c) or important (cj1484c and cj1485c) for Cco activity; Cj1483 is a CcoH homologue, but Cj1484 (designated CcoZ) has structural similarity to MSMEG_4692, involved in Qcr-oxidase supercomplex formation in Mycobacterium smegmatis. Blue-native polyacrylamide gel electrophoresis of detergent solubilised membranes revealed three major bands, one of which contained CcoZ along with Qcr and oxidase subunits. Deletion of putative copper trafficking genes ccoI (cj1155c) and ccoS (cj1154c) abolished Cco activity, which was partially restored by addition of copper during growth, while inactivation of cj0369c encoding a CcoG homologue led to a partial reduction in Cco activity. Deletion of an operon encoding PCuAC (Cj0909) and Sco (Cj0911) periplasmic copper chaperone homologues reduced Cco activity, which was partially restored in the cj0911 mutant by exogenous copper. Phenotypic analyses of gene deletions in the cj1161c–1166c cluster, encoding several genes involved in intracellular metal homeostasis, showed that inactivation of copA (cj1161c), or copZ (cj1162c) led to both elevated intracellular Cu and reduced Cco activity, effects exacerbated at high external Cu. Our work has therefore identified (i) additional Cco subunits, (ii) a previously uncharacterized set of genes linking copper trafficking and Cco activity, and (iii) connections with Cu homeostasis in this important pathogen.

Reconstructing evolutionary trajectories and transitions that have shaped floral diversity relies heavily on the phylogenetic framework on which traits are modelled. In this study, we focus on the angiosperm order Ranunculales, sister to all other eudicots, to unravel higher-level relationships, especially those tied to evolutionary transitions in flower symmetry within the family Papaveraceae. This family presents an astonishing array of floral diversity, with actinomorphic, disymmetric (two perpendicular symmetry axes), and zygomorphic flowers. We generated nuclear and plastid datasets using the Angiosperms353 universal probe set for target capture sequencing (of 353 single-copy nuclear ortholog genes), together with publicly available transcriptome and plastome data mined from open-access online repositories. We relied on the fossil record of the order Ranunculales to date our phylogenies and to establish a timeline of events. Our phylogenomic workflow shows that nuclear-plastid incongruence accompanies topological uncertainties in Ranunculales. A cocktail of incomplete lineage sorting, post-hybridization introgression, and extinction following rapid speciation most likely explain the observed knots in the topology. These knots coincide with major floral symmetry transitions and thus obscure the order of evolutionary events.

Protist plankton can be divided into three main groups: phytoplankton, zooplankton, and mixoplankton. In situ methods for studying phytoplankton and zooplankton are relatively straightforward since they generally target chlorophyll/photosynthesis or grazing activity, while the integration of both processes within a single cell makes mixoplankton inherently challenging to study. As a result, we understand less about mixoplankton physiology and their role in food webs, biogeochemical cycling, and ecosystems compared to phytoplankton and zooplankton. In this paper, we posit that by merging conventional techniques, such as microscopy and physiological data, with innovative methods like in situ single-cell sorting and omics datasets, in conjunction with a diverse array of modeling approaches ranging from single-cell modeling to comprehensive Earth system models, we can propel mixoplankton research into the forefront of aquatic ecology. We present eight crucial research questions pertaining to mixoplankton and mixotrophy, and briefly outline a combination of existing methods and models that can be used to address each question. Our intent is to encourage more interdisciplinary research on mixoplankton, thereby expanding the scope of data acquisition and knowledge accumulation for this understudied yet critical component of aquatic ecosystems.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

ObjectiveTo characterize how the proximity of deep brain stimulation (DBS) active contact locations relative to the cerebellothalamic tract (CTT) affect clinical outcomes in patients with essential tremor (ET).BackgroundDBS is an effective treatment for refractory ET. However, the role of the CTT in mediating the effect of DBS for ET is not well characterized. 7-Tesla (T) MRI-derived tractography provides a means to measure the distance between the active contact and the CTT more precisely.MethodsA retrospective review was conducted of 12 brain hemispheres in 7 patients at a single center who underwent 7T MRI prior to ventral intermediate nucleus (VIM) DBS lead placement for ET following failed medical management. 7T-derived diffusion tractography imaging was used to identify the CTT and was merged with the post-operative CT to calculate the Euclidean distance from the active contact to the CTT. We collected optimized stimulation parameters at initial programing, 1- and 2-year follow up, as well as a baseline and postoperative Fahn-Tolosa-Marin (FTM) scores.ResultsThe therapeutic DBS current mean (SD) across implants was 1.8 mA (1.8) at initial programming, 2.5 mA (0.6) at 1 year, and 2.9 mA (1.1) at 2-year follow up. Proximity of the clinically-optimized active contact to the CTT was 3.1 mm (1.2), which correlated with lower current requirements at the time of initial programming (R2 = 0.458, p = 0.009), but not at the 1- and 2-year follow up visits. Subjects achieved mean (SD) improvement in tremor control of 77.9% (14.5) at mean follow-up time of 22.2 (18.9) months. Active contact distance to the CTT did not predict post-operative tremor control at the time of the longer term clinical follow up (R2 = -0.073, p = 0.58).ConclusionActive DBS contact proximity to the CTT was associated with lower therapeutic current requirement following DBS surgery for ET, but therapeutic current was increased over time. Distance to CTT did not predict the need for increased current over time, or longer term post-operative tremor control in this cohort. Further study is needed to characterize the role of the CTT in long-term DBS outcomes.

High viral tolerance coupled with an extraordinary regulation of the immune response makes bats a great model to study host-pathogen evolution. Although many immune-related gene gains and losses have been previously reported in bats, important gene families such as antimicrobial peptides (AMPs) remain understudied. We built an exhaustive bioinformatic pipeline targeting the major gene families of defensins and cathelicidins to explore AMP diversity and analyze their evolution and distribution across six bat families. A combination of manual and automated procedures identified 29 AMP families across queried species, with α-, β-defensins, and cathelicidins representing around 10% of AMP diversity. Gene duplications were inferred in both α-defensins, which were absent in five species, and three β-defensin gene subfamilies, but cathelicidins did not show significant shifts in gene family size and were absent in Anoura caudifer and the pteropodids. Based on lineage-specific gains and losses, we propose diet and diet-related microbiome evolution may determine the evolution of α- and β-defensins gene families and subfamilies. These results highlight the importance of building species-specific libraries for genome annotation in non-model organisms and shed light on possible drivers responsible for the rapid evolution of AMPs. By focusing on these understudied defenses, we provide a robust framework for explaining bat responses to pathogens.

IntroductionThe classic psychedelic psilocybin, found in some mushroom species, has received renewed interest in clinical research, showing potential mental health benefits in preliminary trials. Naturalistic use of psilocybin outside of research settings has increased in recent years, though data on the public health impact of such use remain limited.MethodsThis prospective, longitudinal study comprised six sequential automated web-based surveys that collected data from adults planning to take psilocybin outside clinical research: at time of consent, 2 weeks before, the day before, 1–3 days after, 2–4 weeks after, and 2–3 months after psilocybin use.ResultsA sample of 2,833 respondents completed all baseline assessments approximately 2 weeks before psilocybin use, 1,182 completed the 2–4 week post-use survey, and 657 completed the final follow-up survey 2–3 months after psilocybin use. Participants were primarily college-educated White men residing in the United States with a prior history of psychedelic use; mean age = 40 years. Participants primarily used dried psilocybin mushrooms (mean dose = 3.1 grams) for “self-exploration” purposes. Prospective longitudinal data collected before and after a planned psilocybin experience on average showed persisting reductions in anxiety, depression, and alcohol misuse, increased cognitive flexibility, emotion regulation, spiritual wellbeing, and extraversion, and reduced neuroticism and burnout after psilocybin use. However, a minority of participants (11% at 2–4 weeks and 7% at 2–3 months) reported persisting negative effects after psilocybin use (e.g., mood fluctuations, depressive symptoms).DiscussionResults from this study, the largest prospective survey of naturalistic psilocybin use to date, support the potential for psilocybin to produce lasting improvements in mental health symptoms and general wellbeing.

ObjectiveTo determine the impact of a cannabidiol (CBD) and cannabidiolic acid (CBDA) rich hemp product on acute post-operative pain in dogs following a tibial plateau leveling osteotomy (TPLO), and to evaluate for changes in early bone healing, serum chemistry profiles, and complete blood counts.MethodsIn this randomized, placebo controlled, blinded clinical trial, 44 client-owned dogs were assigned to receive either a CBD/CBDA product dosed at 2–2.5 mg/kg PO every 12 h or a placebo for 4 weeks following a TPLO. Variables evaluated before (week 0), and at 2 and 4 weeks post-operatively included standardized veterinary assessments for pain score, weight-bearing, and lameness, the Canine Brief Pain Inventory (pain interference score–PIS, pain severity score–PSS), and serum biochemistry. Complete blood counts were performed at weeks 0 and 4. Additionally, orthogonal radiographs evaluating the degree of healing were taken at week 4. A mixed model analysis, analyzing changes of variables of interest from enrollment baseline to all other time points was utilized, with a p-value ≤ 0.05 considered significant.ResultsOf the 44 enrolled patients, 3 were lost to follow up and excluded from analysis. No significant differences were noted between placebo (n = 19) and CBD/CBDA (n = 22) groups at any point in pain score, degree of lameness, degree of weight-bearing, PIS, PSS, or radiographic healing of the osteotomy. A significant finding of elevation of ALP above normal reference range in the treatment group was identified (p = 0.02) and eosinophil count was affected by treatment (p = 0.01), increasing from baseline in placebo and decreasing in treatment groups. Finally, a significant difference (p = 0.03) was noted at 2 weeks post-operatively where 4 patients in the placebo group and no treatment patients received trazodone to facilitate activity restrictions.Clinical significanceUse of a CBD/CBDA rich hemp product dosed at 2–2.5 mg/kg PO every 12 h did not have a significant impact on pain or delay early bone healing. A statistically significant increase in ALP, decrease in eosinophils, and reduced use of trazodone was identified in the treatment group.

ObjectiveGait dysfunction is one of the most difficult motor signs to treat in patients with Parkinson's disease (PD). Understanding its pathophysiology and developing more effective therapies for parkinsonian gait dysfunction will require preclinical studies that can quantitatively and objectively assess the spatial and temporal features of gait.DesignWe developed a novel system for measuring volitional, naturalistic gait patterns in non-human primates, and then applied the approach to characterize the progression of parkinsonian gait dysfunction across a sequence of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treatments that allowed for intrasubject comparisons across mild, moderate, and severe stages.ResultsParkinsonian gait dysfunction was characterized across treatment levels by a slower stride speed, increased time in both the stance and swing phase of the stride cycle, and decreased cadence that progressively worsened with overall parkinsonian severity. In contrast, decreased stride length occurred most notably in the moderate to severe parkinsonian state.ConclusionThe results suggest that mild parkinsonism in the primate model of PD starts with temporal gait deficits, whereas spatial gait deficits manifest after reaching a more severe parkinsonian state overall. This study provides important context for preclinical studies in non-human primates studying the neurophysiology of and treatments for parkinsonian gait.

PremiseWe investigated whether phylogenetic conservatism can improve the performance of seed germinability prediction models. Previous studies in tallgrass prairie and alpine meadow revealed that seed morphological traits demonstrate phylogenetic conservatism. We hypothesized that phylogenetic conservatism in seed traits could help predict the seed germinability, under the assumption that seed traits contain phylogenetic signals.MethodsWe measured seed germination percentage and seed morphological traits (seed mass, seed height, and seed surface area) on 34 native species from shortgrass prairie in North America. We supplemented these data with similar data from the literature on 11 more species. We calculated the robustness of the phylogenetic signal of each trait to the number of species sampled. We also compressed the phylogenetic distance matrix to a two-dimensional space, and applied the Akaike information criterion to evaluate the effects of phylogeny on seed germinability prediction models.Key resultsWe found weak but significant phylogenetic signals in seed mass and seed height in the full data set. These phylogenetic signals were not able to improve seed germinability prediction model performance among shortgrass prairie species. Our robustness tests of phylogenetic signals using random sub-sampling showed that the detection rate of phylogenetic signals in seed mass was increased along with the expansion of species pool, and nearly 100% at 40 species. However, the detection rate of phylogenetic signals in seed height was constantly low, around 20%.ConclusionWhen the phylogenetic signals are weak, the phylogenetic position does not improve germinability prediction model performance. Therefore, phylogenetic signals detected during a single species pool calculation may not accurately reflect the phylogenetic conservatism of the trait in a plant community. We suggest testing for robustness of phylogenetic signals using random sub-sampling tests.

Congenital hyperinsulinism is characterised by the inappropriate release of insulin during hypoglycaemia. This potentially life-threatening disorder can occur in isolation, or present as a feature of syndromic disease. Establishing the underlying aetiology of the hyperinsulinism is critical for guiding medical management of this condition especially in children with diazoxide-unresponsive hyperinsulinism where the underlying genetics determines whether focal or diffuse pancreatic disease is present. Disease-causing single nucleotide variants affecting over 30 genes are known to cause persistent hyperinsulinism with mutations in the KATP channel genes (ABCC8 and KCNJ11) most commonly identified in children with severe persistent disease. Defects in methylation, changes in chromosome number, and large deletions and duplications disrupting multiple genes are also well described in congenital hyperinsulinism, further highlighting the genetic heterogeneity of this condition. Next-generation sequencing has revolutionised the approach to genetic testing for congenital hyperinsulinism with targeted gene panels, exome, and genome sequencing being highly sensitive methods for the analysis of multiple disease genes in a single reaction. It should though be recognised that limitations remain with next-generation sequencing with no single application able to detect all reported forms of genetic variation. This is an important consideration for hyperinsulinism genetic testing as comprehensive screening may require multiple investigations.

Carbon dioxide is a waste product of human metabolism that is unwanted in the indoor atmosphere. Here we test advanced materials that could be used to pump away carbon dioxide and thereby improve indoor air quality. Metal-organic framework (MOF) structures have a variety of properties that make them attractive for direct air capture (DAC) of CO2. In this study, Mg2 (dobpdc4- = 4,4-dioxidobiphenyl-3,3-dicarboxylate) and m-2-m-Mg2(dobpdc) were evaluated for their ability to adsorb and desorb CO2 at ambient atmospheric conditions via a thermal swing. The Mg2(dobpdc) sample did not show appreciable interaction with CO2 in contrast to the diamine-appended version. The molar occupancy of CO2 on the active sites of m-2-m-Mg2 (dobpdc) (m-2-m = N,N-dimethylethylenediamine) during adsorption and desorption was in the range of 5.6–11.8%. These experiments showed the rapid ad- and de-sorption of CO2 by the MOF. Relative humidity was seen to be an important variable and the performance of the tested structures decreased slightly after several cycles of use.

The acute subjective effects of psychedelics are responsive to users’ expectations and surroundings (i.e., “set and setting”). Accordingly, a great deal of thought has gone into designing the psychosocial context of psychedelic administration in clinical settings. But what theoretical paradigms inform these considerations about set and setting? Here, we describe several historical, sociological influences on current psychedelic administration in mainstream European and American clinical research settings, including: indigenous practices, new age spirituality from the 1960s, psychodynamic/psychoanalytic approaches, and cognitive-behavioral approaches. We consider each of these paradigms and determine that cognitive-behavioral therapies, including newer branches such as acceptance and commitment therapy (ACT), have the strongest rationale for psychedelic-assisted psychotherapy going forward. Our primary reasons for advocating for cognitive-behavioral approaches include, (1) they avoid issues of cultural insensitivity, (2) they make minimal speculative assumptions about the nature of the mind and reality, (3) they have the largest base of empirical support for their safety and effectiveness outside of psychedelic therapy. We then propose several concepts from cognitive-behavioral therapies such as CBT, DBT, and ACT that can usefully inform the preparation, session, and integration phases of psychedelic psychotherapy. Overall, while there are many sources from which psychedelic psychotherapy could draw, we argue that current gold-standard, evidence-based psychotherapeutic paradigms provide the best starting point in terms of safety and efficacy.

Operant behavioral economic methods are increasingly used in basic research on the efficacy of reinforcers as well as in large-scale applied research (e.g., evaluation of empirical public policy). Various methods and strategies have been put forward to assist discounting researchers in conducting large-scale research and detecting irregular response patterns. Although rule-based approaches are based on well-established behavioral patterns, these methods for screening discounting data make assumptions about decision-making patterns that may not hold in all cases and across different types of choices. Without methods well-suited to the observed data, valid data could be omitted or invalid data could be included in study analyses, which subsequently affects study power, the precision of estimates, and the generality of effects. This review and demonstration explore existing approaches for characterizing discounting and presents a novel, data-driven approach based on Latent Class Analysis. This approach (Latent Class Mixed Modeling) characterizes longitudinal patterns of choice into classes, the goal of which is to classify groups of responders that differ characteristically from the overall sample of discounters. In the absence of responders whose behavior is characteristically distinct from the greater sample, modern approaches such as mixed-effects models are robust to less-systematic data series. This approach is discussed, demonstrated with a publicly available dataset, and reviewed as a potential supplement to existing methods for inspecting and screening discounting data.

National Oceanic and Atmospheric Administration’s Coral Reef Conservation Program supports the National Coral Reef Monitoring Program (NCRMP) in the United States Pacific, Atlantic, Caribbean, and Gulf of Mexico. NCRMP conducts standardized observations of biological, climatic, and socioeconomic indicators across American Samoa, Guam, the Main Hawaiian Islands, the Northwestern Hawaiian Islands, the Northern Mariana Islands, the Pacific Remote Islands, Florida, the Flower Garden Banks, Puerto Rico, and the United States Virgin Islands. NCRMP provides periodic, national-level assessments of the status of United States coral reef ecosystems and communities connected to them. In 2014, NCRMP partnered with the University of Maryland Center for Environmental Science on an unprecedented collaboration between federal and jurisdictional/state agencies, academia, and non-governmental organizations to synthesize NCRMP data into a reporting format designed to be accessible and relevant to the public and policy makers. The process involved multi-year data analyses of key benthic, fish, and climate indicators. In populated jurisdictions, socioeconomic data were integrated to assess public support for management actions, participation in pro-environmental behaviors, and awareness of threats to coral reefs. Jurisdictions were scored using a report-card scale (0–100%) by establishing references for each indicator using best-available historical data or expert opinion where historical data did not exist or were not statistically comparable. Despite overall ecosystem scores of Fair for all combined Atlantic (70%) and Pacific (74%) jurisdictions, the current trend is downward with a majority of United States coral reefs declining and vulnerable to further degradation. Remote, uninhabited reefs had an advantage with respect to reef fish population scores, i.e., Flower Garden Banks (85%) and Pacific Remote Islands (93%), when compared to populated location scores, i.e., Puerto Rico (63%) and Main Hawaiian Islands (66%). All coral reefs are highly impacted by climate change, and climate impacts were more pronounced than expected on remote reefs, i.e., the Northwestern Hawaiian Islands (58%). Presenting results in a report-card style facilitates communication to the public and policy makers, and provides a useful mechanism to garner support for management actions such as expanding protected areas; enforcing existing regulations; increasing climate change education; reducing land-based sources of pollution; and other actions to improve the trajectory of coral reef ecosystem conditions.

For several decades, cross-lagged panel models (CLPM) have been the dominant statistical model in relationship research for investigating reciprocal associations between two (or more) constructs over time. However, recent methodological research has questioned the frequent usage of the CLPM because, amongst other things, the model commingles within-person associations with between-person associations, while most developmental research questions pertain to within-person processes. Furthermore, the model presumes that there are no third variables that confound the relationships between the longitudinally assessed variables. Therefore, the usage of alternative models such as the Random-Intercept Cross-Lagged Panel Model (RI-CLPM) or the Latent Curve Model with Structured Residuals (LCM-SR) has been suggested. These models separate between-person from within-person variation and they also control for time constant covariates. However, there might also be third variables that are not stable but rather change across time and that can confound the relationships between the variables studied in these models. In the present article, we explain the differences between the two types of confounders and investigate how they affect the parameter estimates of within-person models such as the RI-CLPM and the LCM-SR.

High consumer demand for cannabidiol (CBD) has made high-CBD hemp (Cannabis sativa) an extremely high-value crop. However, high demand has resulted in the industry developing faster than the research, resulting in the sale of many hemp accessions with inconsistent performance and chemical profiles. These inconsistencies cause significant economic and legal problems for growers interested in producing high-CBD hemp. To determine the genetic and phenotypic consistency in available high-CBD hemp varieties, we obtained seed or clones from 22 different named accessions meant for commercial production. Genotypes (∼48,000 SNPs) and chemical profiles (% CBD and THC by dry weight) were determined for up to 8 plants per accession. Many accessions–including several with the same name–showed little consistency either genetically or chemically. Most seed-grown accessions also deviated significantly from their purported levels of CBD and THC based on the supplied certificates of analysis. Several also showed evidence of an active tetrahydrocannabinolic acid (THCa) synthase gene, leading to unacceptably high levels of THC in female flowers. We conclude that the current market for high-CBD hemp varieties is highly unreliable, making many purchases risky for growers. We suggest options for addressing these issues, such using unique names and developing seed and plant certification programs to ensure the availability of high-quality, verified planting materials.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established therapy for Parkinson’s disease motor symptoms. The ideal site for implantation within STN, however, remains controversial. While many argue that placement of a DBS lead within the sensorimotor territory of the STN yields better motor outcomes, others report similar effects with leads placed in the associative or motor territory of the STN, while still others assert that placing a DBS lead “anywhere within a 6-mm-diameter cylinder centered at the presumed middle of the STN (based on stereotactic atlas coordinates) produces similar clinical efficacy.” These discrepancies likely result from methodological differences including targeting preferences, imaging acquisition and the use of brain atlases that do not account for patient-specific anatomic variability. We present a first-in-kind within-patient demonstration of severe mood side effects and minimal motor improvement in a Parkinson’s disease patient following placement of a DBS lead in the limbic/associative territory of the STN who experienced marked improvement in motor benefit and resolution of mood side effects following repositioning the lead within the STN sensorimotor territory. 7 Tesla (7 T) magnetic resonance imaging (MRI) data were used to generate a patient-specific anatomical model of the STN with parcellation into distinct functional territories and computational modeling to assess the relative degree of activation of motor, associative and limbic territories.

Many recent developments in machine learning have come from the field of “deep learning,” or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. The high dimensionality and large amounts of noise present in EEG data, coupled with the relatively low number of examples (trials) that can be reasonably obtained from a sample of human subjects, lead to difficulty training deep learning models. Even when a model successfully converges in training, significant overfitting can occur despite the presence of regularization techniques. To help alleviate these problems, we present a new method of “paired trial classification” that involves classifying pairs of EEG recordings as coming from the same class or different classes. This allows us to drastically increase the number of training examples, in a manner akin to but distinct from traditional data augmentation approaches, through the combinatorics of pairing trials. Moreover, paired trial classification still allows us to determine the true class of a novel example (trial) via a “dictionary” approach: compare the novel example to a group of known examples from each class, and determine the final class via summing the same/different decision values within each class. Since individual trials are noisy, this approach can be further improved by comparing a novel individual example with a “dictionary” in which each entry is an average of several examples (trials). Even further improvements can be realized in situations where multiple samples from a single unknown class can be averaged, thus permitting averaged signals to be compared with averaged signals.

BackgroundObservational data and preliminary studies suggest serotonin 2A agonist psychedelics may hold potential in treating a variety of substance use disorders (SUDs), including opioid use disorder (OUD).AimsThe study aim was to describe and analyze self-reported cases in which naturalistic psychedelic use was followed by cessation or reduction in other substance use.MethodsAn anonymous online survey of individuals reporting cessation or reduction in cannabis, opioid, or stimulant use following psychedelic use in non-clinical settings.ResultsFour hundred forty-four respondents, mostly in the USA (67%) completed the survey. Participants reported 4.5 years of problematic substance use on average before the psychedelic experience to which they attributed a reduction in drug consumption, with 79% meeting retrospective criteria for severe SUD. Most reported taking a moderate or high dose of LSD (43%) or psilocybin-containing mushrooms (29%), followed by significant reduction in drug consumption. Before the psychedelic experience 96% met SUD criteria, whereas only 27% met SUD criteria afterward. Participants rated their psychedelic experience as highly meaningful and insightful, with 28% endorsing psychedelic-associated changes in life priorities or values as facilitating reduced substance misuse. Greater psychedelic dose, insight, mystical-type effects, and personal meaning of experiences were associated with greater reduction in drug consumption.ConclusionsWhile these cross-sectional and self-report methods cannot determine whether psychedelics caused changes in drug use, results suggest the potential that psychedelics cause reductions in problematic substance use, and support additional clinical research on psychedelic-assisted treatment for SUD.

Background: Intra-articular stem cell therapy may help alleviate lameness caused by osteoarthritis in dogs. Umbilical cord-derived stem cell (UMSC) therapy has not yet been investigated in a veterinary clinical study. We hypothesized that dogs treated with intra-articular UMSC will have improved limb function and quality of life when compared to dogs treated with a saline placebo injection.Methods: This was a prospective, double-blinded, placebo-controlled clinical trial in client-owned dogs with chronic elbow osteoarthritis with a follow-up time of 6 months. Dogs were assigned to receive intra-articular UMSC (n = 38) or a saline placebo intra-articular injection (n = 30). Outcome measures included the Canine Brief Pain Inventory score (CBPI) and peak vertical force (PVF) from force-platform gait analysis. Treatment was considered successful when there was a decrease in the Pain Severity Score of at least one and a decrease in the Pain Interference Score of at least one from baseline. Success rates and PVF were compared between groups.Results: No adverse effects associated with UMSC were noted. Of the dogs completing the study, treatment success in the UMSC (n = 28) vs. placebo groups (n = 23) was observed in 54 vs. 28% of dogs at 1 month, 50 vs. 27% at 3 months, and 46 vs. 14% at 6 months, respectively. Success rate in the UMSC group was significantly higher than the placebo group at 1 and 6 months after treatment. However, no differences in PVF of the affected limb over time was observed in either group.Conclusions: Intra-articular UMSC for osteoarthritis may improve clinical signs based on owner observations.

Low levels of desire and arousal are the primary sexual dysfunctions in women, necessitating neurobiological studies of sexual motivation in female animal models. As the mesocorticolimbic system is a primary neural circuit underlying sexual motivation, the goal of this study was to test the hypothesis that medial prefrontal cortex (mPFC) glutamate mediates sexual behavior activation of the nucleus accumbens. Glutamatergic neurons in the mPFC were activated by sex behavior, and these sex-activated cells shown to project to the nucleus accumbens. During sexual interactions with the male, glutamate transients recorded in the nucleus accumbens of female hamsters were specifically associated with the receipt of intromissions from the male. Further, inhibition of the mPFC during sex significantly decreased nucleus accumbens activation. Glutamatergic medial prefrontal cortical input to the nucleus accumbens mediates the activity in the nucleus accumbens during female sexual behavior. These results offer novel insights into the neurobiology of the motivational control of female sexual behavior and provide attractive avenues for pursuing target-specific and clinically-relevant therapies for sexual dysfunction in women.

Previous studies that focused on treating major depressive disorder with conventional deep brain stimulation (DBS) paradigms produced inconsistent results. In this proof-of-concept preclinical study in rats (n = 8), we used novel paradigms of orientation selective DBS for stimulating the complex circuitry crossing the infralimbic cortex, an area considered analogous to human subgenual cingulate cortex. Using functional MRI at 9.4 T, we monitored whole brain responses to varying the electrical field orientation of DBS within the infralimbic cortex. Substantial alterations of functional MRI responses in the amygdala, a major node connected to the infralimbic cortex implicated in the pathophysiology of depression, were observed. As expected, the activation cluster near the electrode was insensitive to the changes of the stimulation orientation. Hence, our findings substantiate the ability of orientation selective stimulation (OSS) to recruit neuronal pathways of distinct orientations relative to the position of the electrode, even in complex circuits such as those involved in major depressive disorder. We conclude that OSS is a promising approach for stimulating brain areas that inherently require individualisation of the treatment approach.

Cryptophyte algae are globally distributed photosynthetic flagellates found in freshwater, estuarine, and neritic ecosystems. While cryptophytes can be highly abundant and are consumed by a wide variety of protistan predators, few studies have sought to quantify in situ grazing rates on their populations. Here we show that autumnal grazing rates on in situ communities of cryptophyte algae in Chesapeake Bay are high throughout the system, while growth rates, particularly in the lower bay, were low. Analysis of the genetic diversity of cryptophyte populations within dilution experiments suggests that microzooplankton may be selectively grazing the fastest-growing members of the population, which were generally Teleaulax spp. We also demonstrate that potential grazing rates of ciliates and dinoflagellates on fluorescently labeled (FL) Rhodomonas salina, Storeatula major, and Teleaulax amphioxeia can be high (up to 149 prey predator−1 d−1), and that a Gyrodinium sp. and Mesodinium rubrum could be selective grazers. Potential grazing was highest for heterotrophic dinoflagellates, but due to its abundance, M. rubrum also had a high overall impact. This study reveals that cryptophyte algae in Chesapeake Bay can experience extremely high grazing pressure from phagotrophic protists, and that this grazing likely shapes their community diversity.

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Ciliates from the genus Mesodinium are globally distributed in marine and freshwater ecosystems and may possess either heterotrophic or mixotrophic nutritional modes. Members of the M. major/rubrum species complex photosynthesize by sequestering and maintaining organelles from cryptophyte prey, and under certain conditions form periodic or recurrent blooms (= red tides). Here we present an analysis of the genetic diversity of Mesodinium and cryptophyte populations from 10 environmental samples (8 globally dispersed habitats including 5 Mesodinium blooms), using group-specific primers for Mesodinium partial 18S, ITS, and partial 28S rRNA genes as well as cryptophyte large subunit RuBisCO genes (rbcL). In addition, 22 new cryptophyte and 4 new M. rubrum cultures were used to extract DNA and sequence rbcL and 18S-ITS-28S genes, respectively, in order to provide a stronger phylogenetic context for our environmental sequences. Bloom samples were analyzed from coastal Brazil, Chile, two Northeastern locations in the United States, and the Pribilof Islands within the Bering Sea. Additionally, samples were also analyzed from the Baltic and Barents Seas and coastal California under non-bloom conditions. Most blooms were dominated by a single Mesodinium genotype, with coastal Brazil and Chile blooms composed of M. major and the Eastern USA blooms dominated by M. rubrum variant B. Sequences from all 4 blooms were dominated by T. amphioxeia-like cryptophytes. Non-bloom communities revealed more diverse assemblages of Mesodinium spp., including heterotrophic species and the mixotrophic M. chamaeleon. Similarly, cryptophyte diversity was also higher in non-bloom samples. Our results confirm that Mesodinium blooms may be caused by M. major, as well as multiple variants of M. rubrum, and further implicate T. amphioxeia as the key cryptophyte species linked to these phenomena in temperate and subtropical regions.

The coccolithophore Emiliania huxleyi forms massive blooms and plays a critical role in global elemental cycles, sequestering significant amounts of atmospheric carbon dioxide on geological time scales via production of calcium carbonate coccoliths and emitting dimethyl sulfoniopropionate (DMSP) which has the potential for increasing atmospheric albedo. Because grazing in pelagic systems is a major top-down force structuring microbial communities, the influence of grazers on E. huxleyi populations has been of interest to researchers. Roles of DMSP (and related metabolites) in interactions between E. huxleyi and protist grazers have been investigated, however, little is known about the release of other metabolites that may influence, or be influenced by, such grazing interactions. We used high-resolution mass spectrometry in an untargeted approach to survey the suite of low molecular weight compounds released by four different E. huxleyi strains in response to grazing by the dinoflagellate Oxyrrhis marina. Overall, a strikingly small number of metabolites were detected from E. huxleyi and O. marina cells, but these were distinctly informative to construct metabolic footprints. At most, E. huxleyi strains shared 25% of released metabolites. Furthermore, there appeared to be no unified metabolic response in E. huxleyi strains to grazing; rather these responses were strain specific. Concentrations of several metabolites also positively correlated with grazer activities, including grazing, ingestion, and growth rates; however, no single metabolite responded uniformly across all strains of E. huxleyi tested. Regardless, grazing clearly transformed the constituents of dissolved organic matter produced by these marine microbes. This study addresses several technical challenges, and presents a platform to further study the influence of chemical cues in aquatic systems and demonstrates the impact of strain diversity and grazing on the complexity of dissolved organic matter in marine systems.

Deep brain stimulation (DBS) in the pedunculopontine tegmental nucleus (PPTg) has been proposed to alleviate medically intractable gait difficulties associated with Parkinson’s disease. Clinical trials have shown somewhat variable outcomes, stemming in part from surgical targeting variability, modulating fiber pathways implicated in side effects, and a general lack of mechanistic understanding of DBS in this brain region. Subject-specific computational models of DBS are a promising tool to investigate the underlying therapy and side effects. In this study, a parkinsonian rhesus macaque was implanted unilaterally with an 8-contact DBS lead in the PPTg region. Fiber tracts adjacent to PPTg, including the oculomotor nerve, central tegmental tract, and superior cerebellar peduncle, were reconstructed from a combination of pre-implant 7T MRI, post-implant CT, and post-mortem histology. These structures were populated with axon models and coupled with a finite element model simulating the voltage distribution in the surrounding neural tissue during stimulation. This study introduces two empirical approaches to evaluate model parameters. First, incremental monopolar cathodic stimulation (20Hz, 90µs pulse width) was evaluated for each electrode, during which a right eyelid flutter was observed at the proximal four contacts (-1.0 to -1.4mA). These current amplitudes followed closely with model predicted activation of the oculomotor nerve when assuming an anisotropic conduction medium. Second, PET imaging was collected OFF-DBS and twice during DBS (two different contacts), which supported the model predicted activation of the central tegmental tract and superior cerebellar peduncle. Together, subject-specific models provide a framework to more precisely predict pathways modulated by DBS.

We used a multi-voxel classification analysis of functional magnetic resonance imaging (fMRI) data to determine to what extent item-specific information about complex natural scenes is represented in several category-selective areas of human extrastriate visual cortex during visual perception and visual mental imagery. Participants in the scanner either viewed or were instructed to visualize previously memorized natural scene exemplars, and the neuroimaging data were subsequently subjected to a multi-voxel pattern analysis (MVPA) using a support vector machine (SVM) classifier. We found that item-specific information was represented in multiple scene-selective areas: the occipital place area (OPA), parahippocampal place area (PPA), retrosplenial cortex (RSC), and a scene-selective portion of the precuneus/intraparietal sulcus region (PCu/IPS). Furthermore, item-specific information from perceived scenes was re-instantiated during mental imagery of the same scenes. These results support findings from previous decoding analyses for other types of visual information and/or brain areas during imagery or working memory, and extend them to the case of visual scenes (and scene-selective cortex). Taken together, such findings support models suggesting that reflective mental processes are subserved by the re-instantiation of perceptual information in high-level visual cortex. We also examined activity in the fusiform face area (FFA) and found that it, too, contained significant item-specific scene information during perception, but not during mental imagery. This suggests that although decodable scene-relevant activity occurs in FFA during perception, FFA activity may not be a necessary (or even relevant) component of one’s mental representation of visual scenes.

Increased serum uric acid (SUA) is a risk factor for gout and renal and cardiovascular disease. The purpose of this study was to identify genetic factors that affect the variation in SUA in 632 Mexican Americans participants of the San Antonio Family Heart Study (SAFHS). A genome-wide association analysis was performed using the Illumina Human Hap 550K single nucleotide polymorphism (SNP) microarray. We used a linear regression-based association test under an additive model of allelic effect, while accounting for non-independence among family members via a kinship variance component. All analyses were performed in the software package SOLAR. SNPs rs6832439, rs13131257 and rs737267 in solute carrier protein 2 family, member 9 (SLC2A9) were associated with SUA at genome-wide significance (p